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K1 - Range of different types of existing data. Common sources of data - internal, external, open data sets, public and private. Data formats and their importance for analysis. Data architecture - the framework against which data is stored and structured including on premises and cloud

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K1 - Range of different types of existing data. Common sources of data - internal, external, open data sets, public and private. Data formats and their importance for analysis. Data architecture - the framework against which data is stored and structured including on premises and cloud

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Assessed via - Professional Discussion underpinned by Portfolio

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B1 - Manage own time to meet deadlines and manage stakeholder expectations

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B1 - Manage own time to meet deadlines and manage stakeholder expectations

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Assessed via - Professional Discussion underpinned by Portfolio

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Welcome to the Data Technician Apprenticeship! Here you will find all of the information that you need in order to be successful in your apprenticeship

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Welcome to the Data Technician Apprenticeship! Here you will find all of the information that you need in order to be successful in your apprenticeship

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Your Trainer is - Charlotte Whetton

Charlotte.Whetton@bsdc.ac.uk

07972 217 962

Your Apprenticeship Support Officer is - Nicola McLaren

Nicola.Mclaren@bsdc.ac.uk

07443 980 403

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Below you will find some useful links for your apprenticeship:

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Below you will find some useful links for your apprenticeship:

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City and Guilds Website for Data Technician

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City and Guilds Website for Data Technician

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City & Guilds Data Technician qualifications. Leading vocational education and training organisation.

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Keep all your files safe, access them on any device you own and share with just the right people. Create a free pCloud account!

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Data technician _ Institute for Apprenticeships and Technical Education

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K2 - How to access and extract data from a range of already identified sources

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K2 - How to access and extract data from a range of already identified sources

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Assessed via - Scenario Demonstration with Questioning

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K3 - How to collate and format data in line with industry standards

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K3 - How to collate and format data in line with industry standards

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Assessed via - Scenario Demonstration with Questioning

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K4 - Data formats and their importance for analysis Management and presentation tools to visualise and review the characteristics of data Communication tools and technologies for collaborative working

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K4 - Data formats and their importance for analysis Management and presentation tools to visualise and review the characteristics of data Communication tools and technologies for collaborative working

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Assessed via - Professional Discussion underpinned by Portfolio

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K5 - Communication methods, formats and techniques, including: written, verbal, non-verbal, presentation, email, conversation, audience and active listening Range of roles within an organisation, including: customer, manager, client, peer, technical and non-technical.

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K5 - Communication methods, formats and techniques, including: written, verbal, non-verbal, presentation, email, conversation, audience and active listening Range of roles within an organisation, including: customer, manager, client, peer, technical and non-technical.

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Assessed via - Professional Discussion underpinned by Portfolio

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K6 - The value of data to the business. How to undertake blending of data from multiple sources

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K6 - The value of data to the business. How to undertake blending of data from multiple sources

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Assessed via - Professional Discussion underpinned by Portfolio

Assessed via - Scenario Demonstration with Questioning

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K7 - Algorithms, and how they work using a step-by-step solution to a problem, or rules to follow to solve the problem and the potential to use automation

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K7 - Algorithms, and how they work using a step-by-step solution to a problem, or rules to follow to solve the problem and the potential to use automation

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Assessed via - Scenario Demonstration with Questioning

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K8 - How to filter details, focusing on information relevant to the data project

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K8 - How to filter details, focusing on information relevant to the data project

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Assessed via - Scenario Demonstration with Questioning

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K9 - Basic statistical methods and simple data modelling to extract relevant data and normalise unstructured data

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K9 - Basic statistical methods and simple data modelling to extract relevant data and normalise unstructured data

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Assessed via - Scenario Demonstration with Questioning

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K10 -  The range of common data quality issues that can arise e.g. misclassication, duplicate entries, spelling errors, obsolete data, compliance issues and interpretation/ translation of meaning

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K10 -  The range of common data quality issues that can arise e.g. misclassication, duplicate entries, spelling errors, obsolete data, compliance issues and interpretation/ translation of meaning

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Assessed via - Professional Discussion underpinned by Portfolio

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K11 - Different methods of validating data and the importance of taking corrective action

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K11 - Different methods of validating data and the importance of taking corrective action

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Assessed via - Professional Discussion underpinned by Portfolio

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K12 - Communicating the results through basic narrative

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K12 - Communicating the results through basic narrative

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Assessed via - Professional Discussion underpinned by Portfolio

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K13 - Legal and regulatory requirements e.g. Data Protection, Data Security, Intellectual Property Rights (IPR), Data sharing, marketing consent, personal data definition. The ethical use of data.

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K13 - Legal and regulatory requirements e.g. Data Protection, Data Security, Intellectual Property Rights (IPR), Data sharing, marketing consent, personal data definition. The ethical use of data.

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Assessed via - Professional Discussion underpinned by Portfolio

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K14 - The significance of customer issues, problems, business value, brand awareness, cultural awareness/ diversity, accessibility, internal/ external audience, level of technical knowledge and profile in a business context

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K14 - The significance of customer issues, problems, business value, brand awareness, cultural awareness/ diversity, accessibility, internal/ external audience, level of technical knowledge and profile in a business context

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Assessed via - Professional Discussion underpinned by Portfolio

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K15 - The role of data in the context of of the digital world including the use of eternal trusted open data sets, how data underpins every digital interaction and connectedness across the digital landscape including applications, devises, IoT, customer centricity

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K15 - The role of data in the context of of the digital world including the use of eternal trusted open data sets, how data underpins every digital interaction and connectedness across the digital landscape including applications, devises, IoT, customer centricity

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Assessed via - Professional Discussion underpinned by Portfolio

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K16 - Different learning techniques, learning techniques and the breadth and sources of knowledge

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K16 - Different learning techniques, learning techniques and the breadth and sources of knowledge

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Assessed via - Professional Discussion underpinned by Portfolio

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S1 - Source and migrate data from already identified different sources

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S1 - Source and migrate data from already identified different sources

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Assessed via - Scenario Demonstration with Questioning

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Assessed via - Scenario Demonstration with Questioning

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S3 - Summarise and explain gathered data

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S3 - Summarise and explain gathered data

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Assessed via - Scenario Demonstration with Questioning

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S4 - Blend data sets from multiple sources and present in format appropriate to the task

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S4 - Blend data sets from multiple sources and present in format appropriate to the task

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Assessed via - Scenario Demonstration with Questioning

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S5 - Manipulate and link different data sets as required

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S5 - Manipulate and link different data sets as required

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Assessed via - Scenario Demonstration with Questioning

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S6 - Use tools and techniques to identify trends and patterns in data

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S6 - Use tools and techniques to identify trends and patterns in data

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Assessed via - Scenario Demonstration with Questioning

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S7 - Apply basic statistical methods and algorithms to identify trends and patterns in data

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S7 - Apply basic statistical methods and algorithms to identify trends and patterns in data

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Assessed via - Scenario Demonstration with Questioning

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S8 - Apply cross checking techniques for identifying faults and data results for data project requirements

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S8 - Apply cross checking techniques for identifying faults and data results for data project requirements

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Assessed via - Scenario Demonstration with Questioning

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Assessed via - Scenario Demonstration with Questioning

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S10 - Demonstrate the different ways of communicating meaning from data in line with audience requirements

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S10 - Demonstrate the different ways of communicating meaning from data in line with audience requirements

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Assessed via - Professional Discussion underpinned by Portfolio

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S11 - Produce clear and consistent technical documentation using standard organisational templates

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S11 - Produce clear and consistent technical documentation using standard organisational templates

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Assessed via - Professional Discussion underpinned by Portfolio

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S12 - Store, manage and distribute in compliance with data security standards and legislation

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S12 - Store, manage and distribute in compliance with data security standards and legislation

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Assessed via - Professional Discussion underpinned by Portfolio

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S13 - Explain data and results to different audiences in a way that aids understanding

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S13 - Explain data and results to different audiences in a way that aids understanding

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Assessed via - Professional Discussion underpinned by Portfolio

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Assessed via - Professional Discussion underpinned by Portfolio

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S15 - Keep up to date with developments in technologies, trends and innovation using a range of sources

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S15 - Keep up to date with developments in technologies, trends and innovation using a range of sources

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Assessed via - Professional Discussion underpinned by Portfolio

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S16 - Clean data i.e. remove duplicates, typos, duplicate entries, out of date data, parse data (e.g. format telephone numbers according to a national standard) and test and assess confidence in the data and its integrity

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S16 - Clean data i.e. remove duplicates, typos, duplicate entries, out of date data, parse data (e.g. format telephone numbers according to a national standard) and test and assess confidence in the data and its integrity

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Assessed via - Scenario Demonstration with Questioning

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S17 - Operate as part of a multi-functional team

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S17 - Operate as part of a multi-functional team

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Assessed via - Professional Discussion underpinned by Portfolio

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S18 - Prioritise within the context of a project

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S18 - Prioritise within the context of a project

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Assessed via - Scenario Demonstration with Questioning

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B2 - Work independently and take responsibility

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B2 - Work independently and take responsibility

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Assessed via - Scenario Demonstration with Questioning

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Assessed via - Scenario Demonstration with Questioning

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Assessed via - Scenario Demonstration with Questioning

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B5 - Work with a range of internal and external customers

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B5 - Work with a range of internal and external customers

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Assessed via - Professional Discussion underpinned by Portfolio

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B6 - Value difference and be sensitive to the needs of others

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B6 - Value difference and be sensitive to the needs of others

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Assessed via - Professional Discussion underpinned by Portfolio

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Data Technician - Portfolio of Evidence Checklist

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Data Technician - Declaration of Authenticity

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Workbook 1 - Introduction to Data Security

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Workbook 1 - Introduction to Data Security

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Data Technician Workbook 1 - Introduction to Data Security

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Assignment 1 - Data Legislation and Security

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Assignment 1 - Data Legislation and Security

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Level 3 Data Technician Apprenticeship - Assignment 1 - Data Legislation and Security

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Level 3 Data Technician Apprenticeship - Assignment 2 - Data Types and Sources

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Data Technician Workbook 2 - Data Sources

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Workbook 3 - The Value of Data and Data Quality

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Workbook 3 - The Value of Data and Data Quality

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Data Technician Workbook 3 - The Value of Data and Data Quality

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Assignment 3 - The Value of Data and Data Quality

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Assignment 3 - The Value of Data and Data Quality

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Level 3 Data Technician Apprenticeship - Assignment 3 - The Value of Data and Data Quality

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Data Technician Workbook 4 - Analysis and Algorithms

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Level 3 Data Technician Apprenticeship - Assignment 4 - Analysis and Algorithms

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Assignment - Summer Portfolio evidence production

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Assignment - Summer Portfolio evidence production

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Worksheet 1 - Reflection on KSB's - Professional Discussion style questions

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Worksheet 1 - Reflection on KSB's - Professional Discussion style questions

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Workbook 5 - Auditing and Communicating Data

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Workbook 5 - Auditing and Communicating Data

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Data Technician Workbook 5 - Auditing and Communicating Data Results

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Assignment 5 - Auditing and Communicating Data

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Assignment 5 - Auditing and Communicating Data

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Level 3 Data Technician Apprenticeship - Assignment 5 - Auditing and Communicating Data

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EPA Information including Grading Descriptors

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EPA Information including Grading Descriptors

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9665-701 Scenario Demonstration Sample - Office Printers

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9665-701 Scenario Demonstration Sample - Warehouse printers

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For completion prior to gateway by apprentice and assessor/employer

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9665-12 Providers and Employers Recording Forms Pack - Data Technician

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End Point Assessment Review Report

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Data Technician Scenario Demonstration - Medical - Copy

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Scenario Demonstration Spreadsheet 1 - Medical - Consultation Room Devices

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Scenario Demonstration Spreadsheet 1 - Medical - Consultation Room Devices

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9665-701 Scenario Demonstration Sample - Medical - Consultation Room Devices

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Scenario Demonstration Spreadsheet 2 - Medical - Diagnostic Equipment Devices

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Scenario Demonstration Spreadsheet 2 - Medical - Diagnostic Equipment Devices

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9665-701 Scenario Demonstration Sample - Medical - Diagnostic Equipment Devices

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Data Technician Scenario Demonstration - Car

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Scenario Demonstration - Motor Vehicle 1 - Assembly Line Machinery

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Scenario Demonstration - Motor Vehicle 1 - Assembly Line Machinery

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9665-701 Scenario Demonstration Sample - Vehicle - Assembly Line Machinery

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Scenario Demonstration Motor Vehicle 2 - Vehicle Diagnostic Equipment

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Scenario Demonstration Motor Vehicle 2 - Vehicle Diagnostic Equipment

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9665-701 Scenario Demonstration Sample - Vehicle - Vehicle Diagnostic Equipment

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Data Technician Scenario Demonstration - Clothing

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Scenario Demonstration - Clothing 1 - Garment Processing Equipment

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Scenario Demonstration - Clothing 1 - Garment Processing Equipment

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9665-701 Scenario Demonstration Sample - Clothing  - Garment Processing Equipment

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Scenario Demonstration - Clothing 2 - Warehouse Handling Equipment

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Scenario Demonstration - Clothing 2 - Warehouse Handling Equipment

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9665-701 Scenario Demonstration Sample - Clothing  - Warehouse Handling Equipment